Stream computing may well perform real-time analysis and processing on the massive data flow. Accordingly, the stream computing may be widely applied in social network, blog, email, video, news, phone record, transmission data, and electronic sensor. When the stream computing is utilized to perform data analysis and processing, a large amount of intermediate data that needs persistence may be generated. Currently, methods of data persistence in the stream computing mainly include a synchronous mode and an asynchronous mode. In particular, the synchronous mode needs to wait for the executing result of an operation to ensure the data persistence to be transactional when intermediate data persistence is performed. Such procedure causes the process to be in a standby status, thereby affecting the speed of data analysis and processing.
In existing technologies, steps of the asynchronous mode in the data persistence method in the stream computing include: starting, by a management module, a task; extracting, by a processing module, data corresponding to the task from a data source, performing, by the processing module, data processing to generate intermediate data and result data, and storing, by two storage modules, the intermediate data and the result data, respectively. Further, the management module again starts a new task. As such, the asynchronous mode may prevent the speed of the data processing from being affected, and the data analysis and processing can be fast.
In a process of implementing embodiments of the present disclosure, inventors find at least the following issues existing in the existing technologies:
In existing technologies, intermediate data persistence result may not be fed back, such that the intermediate data persistence can hardly be ensured to be transactional.